Systems and methods for generating basket and item quantity predictions using machine learning architectures

ABSTRACT

Systems and methods including one or more processors and one or more non-transitory storage devices storing computing instructions configured to run on the one or more processors and perform acts of: generating a feature vector for a user based, at least in part, on historical data pertaining to the user&#39;s previous transactions; generating, using a quantity prediction model of a machine learning architecture, a respective item quantity prediction for each of one or more items included in a predicted basket based, at least in part, on the feature vector for the user; and populating a respective quantity selection option for each of the one or more items included in the predicted basket based on the respective item quantity prediction generated for each of the one or more items. Other embodiments are disclosed herein.

TECHNICAL FIELD

This disclosure relates generally to machine learning architectures thatare configured to generate predictions related to predicted baskets.Exemplary predictions generated by the machine learning architecturesinclude predictions pertaining to sizes of predicted baskets andquantities of each item included in the predicted baskets.

BACKGROUND

Electronic platforms permit users to browse, view, purchase, and/ororder items. During a typical digital shopping experience, users maybrowse items, add items to a digital shopping cart, and purchase theitems. The users also may select options for scheduling the items forpickup or delivery. For certain types of item categories (e.g.,groceries), customers routinely place orders to restock or replenishitems.

In some cases, electronic platform providers may desire to predict itemsto be included in shopping cart for a user's upcoming order. However,doing so is technically challenging for a variety of reasons. Onetechnical challenge relates to accurately predicting a size or quantityof unique items that a user will desire to reorder in an upcomingtransaction. Another technical challenge relates to accuratelypredicting a quantity of each individual item to be added to a digitalshopping cart. The number of unique items and item quantities desired bythe user can vary based on many different factors (e.g., whether theuser recently placed one or more transactions, the number of itemsincluded in the one or more recent transactions, the quantities of itemsin the one or more recent transactions, the frequency at which the userplaces transactions, transient purchasing habits, etc.).

BRIEF DESCRIPTION OF THE DRAWINGS

To facilitate further description of the embodiments, the followingdrawings are provided in which:

FIG. 1 illustrates a front elevational view of a computer system that issuitable for implementing various embodiments of the systems disclosedin FIGS. 3 and 5 ;

FIG. 2 illustrates a representative block diagram of an example of theelements included in the circuit boards inside a chassis of the computersystem of FIG. 1 ;

FIG. 3 illustrates a representative block diagram of a system accordingto certain embodiments;

FIG. 4 illustrates a representative block diagram of a portion of thesystem of FIG. 3 according to certain embodiments;

FIG. 5 illustrates an exemplary interface according to certainembodiments;

FIG. 6 illustrates an exemplary flow diagram according to certainembodiments;

FIG. 7 illustrates an exemplary flow diagram according to certainembodiments;

FIG. 8 illustrates an exemplary flow chart for a method according tocertain embodiments; and

FIG. 9 illustrates an exemplary flow chart for a method according tocertain embodiments.

For simplicity and clarity of illustration, the drawing figuresillustrate the general manner of construction, and descriptions anddetails of well-known features and techniques may be omitted to avoidunnecessarily obscuring the present disclosure. Additionally, elementsin the drawing figures are not necessarily drawn to scale. For example,the dimensions of some of the elements in the figures may be exaggeratedrelative to other elements to help improve understanding of embodimentsof the present disclosure. The same reference numerals in differentfigures denote the same elements.

The terms “first,” “second,” “third,” “fourth,” and the like in thedescription and in the claims, if any, are used for distinguishingbetween similar elements and not necessarily for describing a particularsequential or chronological order. It is to be understood that the termsso used are interchangeable under appropriate circumstances such thatthe embodiments described herein are, for example, capable of operationin sequences other than those illustrated or otherwise described herein.Furthermore, the terms “include,” and “have,” and any variationsthereof, are intended to cover a non-exclusive inclusion, such that aprocess, method, system, article, device, or apparatus that comprises alist of elements is not necessarily limited to those elements, but mayinclude other elements not expressly listed or inherent to such process,method, system, article, device, or apparatus.

The terms “left,” “right,” “front,” “back,” “top,” “bottom,” “over,”“under,” and the like in the description and in the claims, if any, areused for descriptive purposes and not necessarily for describingpermanent relative positions. It is to be understood that the terms soused are interchangeable under appropriate circumstances such that theembodiments of the apparatus, methods, and/or articles of manufacturedescribed herein are, for example, capable of operation in otherorientations than those illustrated or otherwise described herein.

The terms “couple,” “coupled,” “couples,” “coupling,” and the likeshould be broadly understood and refer to connecting two or moreelements mechanically and/or otherwise. Two or more electrical elementsmay be electrically coupled together, but not be mechanically orotherwise coupled together. Coupling may be for any length of time,e.g., permanent or semi-permanent or only for an instant. “Electricalcoupling” and the like should be broadly understood and includeelectrical coupling of all types. The absence of the word “removably,”“removable,” and the like near the word “coupled,” and the like does notmean that the coupling, etc. in question is or is not removable.

As defined herein, two or more elements are “integral” if they arecomprised of the same piece of material. As defined herein, two or moreelements are “non-integral” if each is comprised of a different piece ofmaterial.

As defined herein, “real-time” can, in some embodiments, be defined withrespect to operations carried out as soon as practically possible uponoccurrence of a triggering event. A triggering event can include receiptof data necessary to execute a task or to otherwise process information.Because of delays inherent in transmission and/or in computing speeds,the term “real time” encompasses operations that occur in “near” realtime or somewhat delayed from a triggering event. In a number ofembodiments, “real time” can mean real time less a time delay forprocessing (e.g., determining) and/or transmitting data. The particulartime delay can vary depending on the type and/or amount of the data, theprocessing speeds of the hardware, the transmission capability of thecommunication hardware, the transmission distance, etc. However, in manyembodiments, the time delay can be less than approximately one second,two seconds, five seconds, or ten seconds.

As defined herein, “approximately” can, in some embodiments, mean withinplus or minus ten percent of the stated value. In other embodiments,“approximately” can mean within plus or minus five percent of the statedvalue. In further embodiments, “approximately” can mean within plus orminus three percent of the stated value. In yet other embodiments,“approximately” can mean within plus or minus one percent of the statedvalue.

DESCRIPTION OF EXAMPLES OF EMBODIMENTS

A number of embodiments can include a system. The system can include oneor more processors and one or more non-transitory computer-readablestorage devices storing computing instructions. The computinginstructions can be configured to run on the one or more processors andperform acts of: generating a feature vector for a user based, at leastin part, on historical data pertaining to the user's previoustransactions; selecting one or more items to be included in a predictedbasket for the user; generating, using a quantity prediction model of amachine learning architecture, a respective item quantity prediction foreach of the one or more items included in the predicted basket based, atleast in part, on the feature vector for the user; and populating arespective quantity selection option for each of the one or more itemsincluded in the predicted basket based on the respective item quantityprediction generated for each of the one or more items.

Various embodiments include a method. The method can be implemented viaexecution of computing instructions configured to run at one or moreprocessors and configured to be stored at non-transitorycomputer-readable media. The method can comprise: generating a featurevector for a user based, at least in part, on historical data pertainingto the user's previous transactions; selecting one or more items to beincluded in a predicted basket for the user; generating, using aquantity prediction model of a machine learning architecture, arespective item quantity prediction for each of the one or more itemsincluded in the predicted basket based, at least in part, on the featurevector for the user; and populating a respective quantity selectionoption for each of the one or more items included in the predictedbasket based on the respective item quantity prediction generated foreach of the one or more items.

The system can include one or more processors and one or morenon-transitory computer-readable storage devices storing computinginstructions. The computing instructions can be configured to run on theone or more processors and perform acts of: generating a feature vectorfor a user based, at least in part, on historical data pertaining to theuser's previous transactions; generating likelihood scores for aplurality of items; generating, using a basket composition model of amachine learning architecture, a basket size prediction for a predictedbasket based, at least in part, on the feature vector for the user, thebasket size prediction indicating a number of items to be included inthe predicted basket; and populating the predicted basket with one ormore items, wherein the basket size prediction determines the number ofthe one or more items included in the predicted basket and thelikelihood scores are utilized to select the one or more items from theplurality of items.

Another method can be implemented via execution of computinginstructions configured to run at one or more processors and configuredto be stored at non-transitory computer-readable media. The method cancomprise: generating a feature vector for a user based, at least inpart, on historical data pertaining to the user's previous transactions;generating likelihood scores for a plurality of items; generating, usinga basket composition model of a machine learning architecture, a basketsize prediction for a predicted basket based, at least in part, on thefeature vector for the user, the basket size prediction indicating anumber of items to be included in the predicted basket; and populatingthe predicted basket with one or more items, wherein the basket sizeprediction determines the number of the one or more items included inthe predicted basket and the likelihood scores are utilized to selectthe one or more items from the plurality of items.

Turning to the drawings, FIG. 1 illustrates an exemplary embodiment of acomputer system 100, all of which or a portion of which can be suitablefor (i) implementing part or all of one or more embodiments of thetechniques, methods, and systems and/or (ii) implementing and/oroperating part or all of one or more embodiments of the memory storagemodules described herein. As an example, a different or separate one ofa chassis 102 (and its internal components) can be suitable forimplementing part or all of one or more embodiments of the techniques,methods, and/or systems described herein. Furthermore, one or moreelements of computer system 100 (e.g., a monitor 106, a keyboard 104,and/or a mouse 110, etc.) also can be appropriate for implementing partor all of one or more embodiments of the techniques, methods, and/orsystems described herein. Computer system 100 can comprise chassis 102containing one or more circuit boards (not shown), a Universal SerialBus (USB) port 112, a Compact Disc Read-Only Memory (CD-ROM) and/orDigital Video Disc (DVD) drive 116, and a hard drive 114. Arepresentative block diagram of the elements included on the circuitboards inside chassis 102 is shown in FIG. 2 . A central processing unit(CPU) 210 in FIG. 2 is coupled to a system bus 214 in FIG. 2 . Invarious embodiments, the architecture of CPU 210 can be compliant withany of a variety of commercially distributed architecture families.

Continuing with FIG. 2 , system bus 214 also is coupled to a memorystorage unit 208, where memory storage unit 208 can comprise (i)non-volatile memory, such as, for example, read only memory (ROM) and/or(ii) volatile memory, such as, for example, random access memory (RAM).The non-volatile memory can be removable and/or non-removablenon-volatile memory. Meanwhile, RAM can include dynamic RAM (DRAM),static RAM (SRAM), etc. Further, ROM can include mask-programmed ROM,programmable ROM (PROM), one-time programmable ROM (OTP), erasableprogrammable read-only memory (EPROM), electrically erasableprogrammable ROM (EEPROM) (e.g., electrically alterable ROM (EAROM)and/or flash memory), etc. In these or other embodiments, memory storageunit 208 can comprise (i) non-transitory memory and/or (ii) transitorymemory.

In many embodiments, all or a portion of memory storage unit 208 can bereferred to as memory storage module(s) and/or memory storage device(s).In various examples, portions of the memory storage module(s) of thevarious embodiments disclosed herein (e.g., portions of the non-volatilememory storage module(s)) can be encoded with a boot code sequencesuitable for restoring computer system 100 (FIG. 1 ) to a functionalstate after a system reset. In addition, portions of the memory storagemodule(s) of the various embodiments disclosed herein (e.g., portions ofthe non-volatile memory storage module(s)) can comprise microcode suchas a Basic Input-Output System (BIOS) operable with computer system 100(FIG. 1 ). In the same or different examples, portions of the memorystorage module(s) of the various embodiments disclosed herein (e.g.,portions of the non-volatile memory storage module(s)) can comprise anoperating system, which can be a software program that manages thehardware and software resources of a computer and/or a computer network.The BIOS can initialize and test components of computer system 100 (FIG.1 ) and load the operating system. Meanwhile, the operating system canperform basic tasks such as, for example, controlling and allocatingmemory, prioritizing the processing of instructions, controlling inputand output devices, facilitating networking, and managing files.Exemplary operating systems can comprise one of the following: (i)Microsoft® Windows® operating system (OS) by Microsoft Corp. of Redmond,Wash., United States of America, (ii) Mac® OS X by Apple Inc. ofCupertino, Calif., United States of America, (iii) UNIX® OS, and (iv)Linux® OS. Further exemplary operating systems can comprise one of thefollowing: (i) the iOS® operating system by Apple Inc. of Cupertino,Calif., United States of America, (ii) the Blackberry® operating systemby Research In Motion (RIM) of Waterloo, Ontario, Canada, (iii) theWebOS operating system by LG Electronics of Seoul, South Korea, (iv) theAndroid™ operating system developed by Google, of Mountain View, Calif.,United States of America, (v) the Windows Mobile™ operating system byMicrosoft Corp. of Redmond, Wash., United States of America, or (vi) theSymbian™ operating system by Accenture PLC of Dublin, Ireland.

As used herein, “processor” and/or “processing module” means any type ofcomputational circuit, such as but not limited to a microprocessor, amicrocontroller, a controller, a complex instruction set computing(CISC) microprocessor, a reduced instruction set computing (RISC)microprocessor, a very long instruction word (VLIW) microprocessor, agraphics processor, a digital signal processor, or any other type ofprocessor or processing circuit capable of performing the desiredfunctions. In some examples, the one or more processing modules of thevarious embodiments disclosed herein can comprise CPU 210.

Alternatively, or in addition to, the systems and procedures describedherein can be implemented in hardware, or a combination of hardware,software, and/or firmware. For example, one or more application specificintegrated circuits (ASICs) can be programmed to carry out one or moreof the systems and procedures described herein. For example, one or moreof the programs and/or executable program components described hereincan be implemented in one or more ASICs. In many embodiments, anapplication specific integrated circuit (ASIC) can comprise one or moreprocessors or microprocessors and/or memory blocks or memory storage.

In the depicted embodiment of FIG. 2 , various I/O devices such as adisk controller 204, a graphics adapter 224, a video controller 202, akeyboard adapter 226, a mouse adapter 206, a network adapter 220, andother I/O devices 222 can be coupled to system bus 214. Keyboard adapter226 and mouse adapter 206 are coupled to keyboard 104 (FIGS. 1-2 ) andmouse 110 (FIGS. 1-2 ), respectively, of computer system 100 (FIG. 1 ).While graphics adapter 224 and video controller 202 are indicated asdistinct units in FIG. 2 , video controller 202 can be integrated intographics adapter 224, or vice versa in other embodiments. Videocontroller 202 is suitable for monitor 106 (FIGS. 1-2 ) to displayimages on a screen 108 (FIG. 1 ) of computer system 100 (FIG. 1 ). Diskcontroller 204 can control hard drive 114 (FIGS. 1-2 ), USB port 112(FIGS. 1-2 ), and CD-ROM drive 116 (FIGS. 1-2 ). In other embodiments,distinct units can be used to control each of these devices separately.

Network adapter 220 can be suitable to connect computer system 100 (FIG.1 ) to a computer network by wired communication (e.g., a wired networkadapter) and/or wireless communication (e.g., a wireless networkadapter). In some embodiments, network adapter 220 can be plugged orcoupled to an expansion port (not shown) in computer system 100 (FIG. 1). In other embodiments, network adapter 220 can be built into computersystem 100 (FIG. 1 ). For example, network adapter 220 can be built intocomputer system 100 (FIG. 1 ) by being integrated into the motherboardchipset (not shown), or implemented via one or more dedicatedcommunication chips (not shown), connected through a PCI (peripheralcomponent interconnector) or a PCI express bus of computer system 100(FIG. 1 ) or USB port 112 (FIG. 1 ).

Returning now to FIG. 1 , although many other components of computersystem 100 are not shown, such components and their interconnection arewell known to those of ordinary skill in the art. Accordingly, furtherdetails concerning the construction and composition of computer system100 and the circuit boards inside chassis 102 are not discussed herein.

Meanwhile, when computer system 100 is running, program instructions(e.g., computer instructions) stored on one or more of the memorystorage module(s) of the various embodiments disclosed herein can beexecuted by CPU 210 (FIG. 2 ). At least a portion of the programinstructions, stored on these devices, can be suitable for carrying outat least part of the techniques and methods described herein.

Further, although computer system 100 is illustrated as a desktopcomputer in FIG. 1 , there can be examples where computer system 100 maytake a different form factor while still having functional elementssimilar to those described for computer system 100. In some embodiments,computer system 100 may comprise a single computer, a single server, ora cluster or collection of computers or servers, or a cloud of computersor servers. Typically, a cluster or collection of servers can be usedwhen the demand on computer system 100 exceeds the reasonable capabilityof a single server or computer. In certain embodiments, computer system100 may comprise a portable computer, such as a laptop computer. Incertain other embodiments, computer system 100 may comprise a mobileelectronic device, such as a smartphone. In certain additionalembodiments, computer system 100 may comprise an embedded system.

Turning ahead in the drawings, FIG. 3 illustrates a block diagram of asystem 300 that can be employed for generating predictions for predictedbaskets, as described in greater detail below. System 300 is merelyexemplary and embodiments of the system are not limited to theembodiments presented herein. System 300 can be employed in manydifferent embodiments or examples not specifically depicted or describedherein. In some embodiments, certain elements or modules of system 300can perform various procedures, processes, and/or activities. In theseor other embodiments, the procedures, processes, and/or activities canbe performed by other suitable elements or modules of system 300.

Generally, therefore, system 300 can be implemented with hardware and/orsoftware, as described herein. In some embodiments, part or all of thehardware and/or software can be conventional, while in these or otherembodiments, part or all of the hardware and/or software can becustomized (e.g., optimized) for implementing part or all of thefunctionality of system 300 described herein.

In some embodiments, system 300 can include a web server 320, anelectronic platform 330, and a machine learning architecture 350. Webserver 320, electronic platform 330, and machine learning architecture350 can each be a computer system, such as computer system 100 (FIG. 1), as described above, and can each be a single computer, a singleserver, or a cluster or collection of computers or servers, or a cloudof computers or servers. In another embodiment, a single computer systemcan host each of two or more of web server 320, electronic platform 330,and machine learning architecture 350. Additional details regarding webserver 320, electronic platform 330, and machine learning architecture350 are described herein.

In many embodiments, system 300 also can comprise user computers 340.User computers 340 can comprise any of the elements described inrelation to computer system 100. In some embodiments, user computers 340can be mobile devices. A mobile electronic device can refer to aportable electronic device (e.g., an electronic device easily conveyableby hand by a person of average size) with the capability to presentaudio and/or visual data (e.g., text, images, videos, music, etc.). Forexample, a mobile electronic device can comprise at least one of adigital media player, a cellular telephone (e.g., a smartphone), apersonal digital assistant, a handheld digital computer device (e.g., atablet personal computer device), a laptop computer device (e.g., anotebook computer device, a netbook computer device), a wearable usercomputer device, or another portable computer device with the capabilityto present audio and/or visual data (e.g., images, videos, music, etc.).Thus, in many examples, a mobile electronic device can comprise a volumeand/or weight sufficiently small as to permit the mobile electronicdevice to be easily conveyable by hand. For examples, in someembodiments, a mobile electronic device can occupy a volume of less thanor equal to approximately 1790 cubic centimeters, 2434 cubiccentimeters, 2876 cubic centimeters, 4056 cubic centimeters, and/or 5752cubic centimeters. Further, in these embodiments, a mobile electronicdevice can weigh less than or equal to 15.6 Newtons, 17.8 Newtons, 22.3Newtons, 31.2 Newtons, and/or 44.5 Newtons.

Exemplary mobile electronic devices can comprise (i) an iPod®, iPhone®,iTouch®, iPad®, MacBook® or similar product by Apple Inc. of Cupertino,Calif., United States of America, (ii) a Blackberry® or similar productby Research in Motion (RIM) of Waterloo, Ontario, Canada, (iii) a Lumia®or similar product by the Nokia Corporation of Keilaniemi, Espoo,Finland, and/or (iv) a Galaxy™ or similar product by the Samsung Groupof Samsung Town, Seoul, South Korea. Further, in the same or differentembodiments, a mobile electronic device can comprise an electronicdevice configured to implement one or more of (i) the iPhone® operatingsystem by Apple Inc. of Cupertino, Calif., United States of America,(ii) the Blackberry® operating system by Research In Motion (RIM) ofWaterloo, Ontario, Canada, (iii) the Palm® operating system by Palm,Inc. of Sunnyvale, Calif., United States, (iv) the Android™ operatingsystem developed by the Open Handset Alliance, (v) the Windows Mobile™operating system by Microsoft Corp. of Redmond, Wash., United States ofAmerica, or (vi) the Symbian™ operating system by Nokia Corp. ofKeilaniemi, Espoo, Finland.

Further still, the term “wearable user computer device” as used hereincan refer to an electronic device with the capability to present audioand/or visual data (e.g., text, images, videos, music, etc.) that isconfigured to be worn by a user and/or mountable (e.g., fixed) on theuser of the wearable user computer device (e.g., sometimes under or overclothing; and/or sometimes integrated with and/or as clothing and/oranother accessory, such as, for example, a hat, eyeglasses, a wristwatch, shoes, etc.). In many examples, a wearable user computer devicecan comprise a mobile electronic device, and vice versa. However, awearable user computer device does not necessarily comprise a mobileelectronic device, and vice versa.

In specific examples, a wearable user computer device can comprise ahead mountable wearable user computer device (e.g., one or more headmountable displays, one or more eyeglasses, one or more contact lenses,one or more retinal displays, etc.) or a limb mountable wearable usercomputer device (e.g., a smart watch). In these examples, a headmountable wearable user computer device can be mountable in closeproximity to one or both eyes of a user of the head mountable wearableuser computer device and/or vectored in alignment with a field of viewof the user.

In more specific examples, a head mountable wearable user computerdevice can comprise (i) Google Glass™ product or a similar product byGoogle Inc. of Menlo Park, Calif., United States of America; (ii) theEye Tap™ product, the Laser Eye Tap™ product, or a similar product byePI Lab of Toronto, Ontario, Canada, and/or (iii) the Raptyr™ product,the STAR 1200™ product, the Vuzix Smart Glasses M100™ product, or asimilar product by Vuzix Corporation of Rochester, N.Y., United Statesof America. In other specific examples, a head mountable wearable usercomputer device can comprise the Virtual Retinal Display™ product, orsimilar product by the University of Washington of Seattle, Wash.,United States of America. Meanwhile, in further specific examples, alimb mountable wearable user computer device can comprise the iWatch™product, or similar product by Apple Inc. of Cupertino, Calif., UnitedStates of America, the Galaxy Gear or similar product of Samsung Groupof Samsung Town, Seoul, South Korea, the Moto 360 product or similarproduct of Motorola of Schaumburg, Ill., United States of America,and/or the Zip™ product, One™ product, Flex™ product, Charge™ product,Surge™ product, or similar product by Fitbit Inc. of San Francisco,Calif., United States of America.

In many embodiments, system 300 can comprise graphical user interfaces(“GUIs”) 345. In the same or different embodiments, GUIs 345 can be partof and/or displayed by computing devices associated with system 300and/or user computers 340, which also can be part of system 300. In someembodiments, GUIs 345 can comprise text and/or graphics (images) baseduser interfaces. In the same or different embodiments, GUIs 345 cancomprise a heads up display (“HUD”). When GUIs 345 comprise a HUD, GUIs345 can be projected onto glass or plastic, displayed in midair as ahologram, or displayed on monitor 106 (FIG. 1 ). In various embodiments,GUIs 345 can be color or black and white. In many embodiments, GUIs 345can comprise an application running on a computer system, such ascomputer system 100, user computers 340, and/or server computer 310. Inthe same or different embodiments, GUI 345 can comprise a websiteaccessed through network 315 (e.g., the Internet). In some embodiments,GUI 345 can comprise an eCommerce website. In the same or differentembodiments, GUI 345 can be displayed as or on a virtual reality (VR)and/or augmented reality (AR) system or display.

In some embodiments, web server 320 can be in data communication throughnetwork 315 (e.g., the Internet) with user computers (e.g., 340). Incertain embodiments, the network 315 may represent any type ofcommunication network, e.g., such as one that comprises the Internet, alocal area network (e.g., a Wi-Fi network), a personal area network(e.g., a Bluetooth network), a wide area network, an intranet, acellular network, a television network, and/or other types of networks.In certain embodiments, user computers 340 can be desktop computers,laptop computers, smart phones, tablet devices, and/or other endpointdevices. Web server 320 can host one or more websites. For example, webserver 320 can host an eCommerce website that allows users to browseand/or search for products, to add products to an electronic shoppingcart, and/or to purchase products, in addition to other suitableactivities.

In many embodiments, web server 320, electronic platform 330, andmachine learning architecture 350 can each comprise one or more inputdevices (e.g., one or more keyboards, one or more keypads, one or morepointing devices such as a computer mouse or computer mice, one or moretouchscreen displays, a microphone, etc.), and/or can each comprise oneor more display devices (e.g., one or more monitors, one or more touchscreen displays, projectors, etc.). In these or other embodiments, oneor more of the input device(s) can be similar or identical to keyboard104 (FIG. 1 ) and/or a mouse 110 (FIG. 1 ). Further, one or more of thedisplay device(s) can be similar or identical to monitor 106 (FIG. 1 )and/or screen 108 (FIG. 1 ). The input device(s) and the displaydevice(s) can be coupled to the processing module(s) and/or the memorystorage module(s) web server 320, electronic platform 330, and machinelearning architecture 350 in a wired manner and/or a wireless manner,and the coupling can be direct and/or indirect, as well as locallyand/or remotely. As an example of an indirect manner (which may or maynot also be a remote manner), a keyboard-video-mouse (KVM) switch can beused to couple the input device(s) and the display device(s) to theprocessing module(s) and/or the memory storage module(s). In someembodiments, the KVM switch also can be part of web server 320,electronic platform 330, and machine learning architecture 350. In asimilar manner, the processing module(s) and the memory storagemodule(s) can be local and/or remote to each other.

In many embodiments, web server 320, electronic platform 330, andmachine learning architecture 350 can be configured to communicate withone or more user computers 340. In some embodiments, user computers 340also can be referred to as customer computers. In some embodiments, webserver 320, electronic platform 330, and machine learning architecture350 can communicate or interface (e.g., interact) with one or morecustomer computers (such as user computers 340) through a network 315(e.g., the Internet). Network 315 can be an intranet that is not open tothe public. Accordingly, in many embodiments, web server 320, electronicplatform 330, and machine learning architecture 350 (and/or the softwareused by such systems) can refer to a back end of system 300 operated byan operator and/or administrator of system 300, and user computers 340(and/or the software used by such systems) can refer to a front end ofsystem 300 used by one or more users 305, respectively. In someembodiments, users 305 can also be referred to as customers, in whichcase, user computers 340 can be referred to as customer computers. Inthese or other embodiments, the operator and/or administrator of system300 can manage system 300, the processing module(s) of system 300,and/or the memory storage module(s) of system 300 using the inputdevice(s) and/or display device(s) of system 300.

Meanwhile, in many embodiments, web server 320, electronic platform 330,and machine learning architecture 350 also can be configured tocommunicate with one or more databases. The one or more databases cancomprise a product database that contains information about products,items, or SKUs (stock keeping units) sold by a retailer. The one or moredatabases can be stored on one or more memory storage modules (e.g.,non-transitory memory storage module(s)), which can be similar oridentical to the one or more memory storage module(s) (e.g.,non-transitory memory storage module(s)) described above with respect tocomputer system 100 (FIG. 1 ). Also, in some embodiments, for anyparticular database of the one or more databases, that particulardatabase can be stored on a single memory storage module of the memorystorage module(s), and/or the non-transitory memory storage module(s)storing the one or more databases or the contents of that particulardatabase can be spread across multiple ones of the memory storagemodule(s) and/or non-transitory memory storage module(s) storing the oneor more databases, depending on the size of the particular databaseand/or the storage capacity of the memory storage module(s) and/ornon-transitory memory storage module(s).

The one or more databases can each comprise a structured (e.g., indexed)collection of data and can be managed by any suitable databasemanagement systems configured to define, create, query, organize,update, and manage database(s). Exemplary database management systemscan include MySQL (Structured Query Language) Database, PostgreSQLDatabase, Microsoft SQL Server Database, Oracle Database, SAP (Systems,Applications, & Products) Database, IBM DB2 Database, and/or NoSQLDatabase.

Meanwhile, communication between or among web server 320, electronicplatform 330, and machine learning architecture 350, and/or the one ormore databases can be implemented using any suitable manner of wiredand/or wireless communication. Accordingly, system 300 can comprise anysoftware and/or hardware components configured to implement the wiredand/or wireless communication. Further, the wired and/or wirelesscommunication can be implemented using any one or any combination ofwired and/or wireless communication network topologies (e.g., ring,line, tree, bus, mesh, star, daisy chain, hybrid, etc.) and/or protocols(e.g., personal area network (PAN) protocol(s), local area network (LAN)protocol(s), wide area network (WAN) protocol(s), cellular networkprotocol(s), powerline network protocol(s), etc.). Exemplary PANprotocol(s) can comprise Bluetooth, Zigbee, Wireless Universal SerialBus (USB), Z-Wave, etc.; exemplary LAN and/or WAN protocol(s) cancomprise Institute of Electrical and Electronic Engineers (IEEE) 802.3(also known as Ethernet), IEEE 802.11 (also known as WiFi), etc.; andexemplary wireless cellular network protocol(s) can comprise GlobalSystem for Mobile Communications (GSM), General Packet Radio Service(GPRS), Code Division Multiple Access (CDMA), Evolution-Data Optimized(EV-DO), Enhanced Data Rates for GSM Evolution (EDGE), Universal MobileTelecommunications System (UMTS), Digital Enhanced CordlessTelecommunications (DECT), Digital AMPS (IS-136/Time Division MultipleAccess (TDMA)), Integrated Digital Enhanced Network (iDEN), EvolvedHigh-Speed Packet Access (HSPA+), Long-Term Evolution (LTE), WiMAX, etc.The specific communication software and/or hardware implemented candepend on the network topologies and/or protocols implemented, and viceversa. In many embodiments, exemplary communication hardware cancomprise wired communication hardware including, for example, one ormore data buses, such as, for example, universal serial bus(es), one ormore networking cables, such as, for example, coaxial cable(s), opticalfiber cable(s), and/or twisted pair cable(s), any other suitable datacable, etc. Further exemplary communication hardware can comprisewireless communication hardware including, for example, one or moreradio transceivers, one or more infrared transceivers, etc. Additionalexemplary communication hardware can comprise one or more networkingcomponents (e.g., modulator-demodulator components, gateway components,etc.).

In certain embodiments, users 305 may operate user computers 340 tobrowse, view, purchase, and/or order items 310 via the electronicplatform 330. For example, the electronic platform 330 may include aneCommerce website that enables users 305 to access interfaces (e.g.,GUIs 345) that display details about items 310, add items 310 to adigital shopping cart 360, and purchase the added items 310. The items310 made available via the electronic platform 330 may generally relateto any type of product and/or service including, but not limited to,products and/or services associated with groceries, household items,entertainment, furniture, apparel, kitchenware, fashion, appliances,sporting goods, electronics, software, etc.

The electronic platform 330 can be configured to generate predictedbaskets 365 for the digital shopping cart 360. A predicted basket 365can represent, or include, one or more items 310 that are presented orsuggested to a user 305 for an upcoming transaction 375 based onpredictions generated by the machine learning architecture 350. Thepredicted baskets 365 can be displayed on GUIs 345 that display thesuggested items 310, and the GUIs 345 may include an option (e.g., aselectable GUI button) that permits the user 305 to quickly and easilyadd the suggested items 310 to a digital shopping cart 360. Thepredicted baskets 365 also can include options that permit users tomodify the contents of the predicted baskets (e.g., to increase anddecrease quantities of items 310 and/or to remove items 310 from thepredicted baskets 365.

To generate the predicted baskets 365, the machine learning architecture350 can be configured to learn user transaction patterns and select theone or more items 310 to be added to the predicted basket 365 based, atleast in part, on the learned user transaction patterns. The manner inwhich the machine learning architecture 350 is trained to learn the usertransaction patterns may vary. Exemplary techniques are described infurther detail below.

Amongst other things, the predicted baskets 365 enable users 305 toeasily restock or repurchase items 310. In certain embodiments, apredicted basket 365 can be pre-filled and/or automatically populatedwith items 310 (and appropriate quantities for each item) withoutrequiring a user to manually select the items 310, and the user caneasily add the items 310 to a digital shopping cart by selecting a GUIoption (e.g., an “Add all to cart”) in a single mouse click and/orsingle tap gesture. The number of unique items 310 added the predictedbasket 365, as well as the quantity of each item 310, can bepersonalized to each user based, at least in part, on user transactionpatterns learned by the machine learning architecture 350. Thismaximizes the probability of the users 305 accepting the proposed items310 and respective predicted quantities, and improves customerengagement and retention on the electronic platform 330. Additionally,these techniques also minimize the time and efforts users' 305 spendbuilding a digital shopping cart 360, and permit easy reordering ofitems 310.

The machine learning architecture 350 can be configured to executevarious predicted basket functions 355 that assist with generating orcreating predicted baskets 365. Exemplary predicted basket functions 355can include functions for selecting items 310 to be included in thepredicted baskets 365. In certain embodiments, the machine learningarchitecture 350 can generate the predicted baskets 365 and select items310 to be included in the predicted baskets 365 using the techniquesdescribed in U.S. patent application Ser. No. 16/779,254 filed on Jan.31, 2020, which is herein incorporated by reference in its entirety.

The machine learning architecture 350 also can be configured to executepredicted basket functions 355 related to generating basket sizepredictions 351, item quantity predictions 352, and/or other types ofpredictions. The basket size predictions 351 can include or indicate anumber of unique items 310 to be included in the predicted basket 365.The item quantity predictions 352 can include or indicate a quantity foreach of the items 310 included in the predicted basket 365.

In certain embodiments, the machine learning architecture 350 generatesthese and other predictions based on an analysis of historical data 370for the users 305. For example, the electronic platform 330 can beconfigured to store historical data 370, which records some or allactivities involving users' 305 interactions with electronic platform330 and/or interactions with items 310 offered through the electronicplatform 330.

In certain embodiments, the historical data 370 stored for each user 305can include information related to transactions 375 conducted by each ofthe users 305. Each transaction 375 can relate to a previous order inwhich one or more items 310 were purchased by a user 305 through eitheran online channel (e.g., via the electronic platform 330) or an offlinechannel (e.g., at a brick-and-mortar location). For example, a typicaltransaction 375 may involve a user 305 purchasing one or more items 310via the electronic platform 330 or offline channel, and scheduling theone or more items 310 for pick-up or delivery. In certain embodiments,the transactions 375 conducted by each user 305, and all historical data370 recorded for the user 305, can be associated with a profileassociated with the user 305.

For each user 305, the historical data 370 can include detailedinformation related to each transaction 375 initiated by the user 305.For example, the electronic platform 330 may store any or all of thefollowing information for each transaction 375 conducted by a user 305:any items 310 that were included in the transaction 375 and/or purchasedin connection with the transactions 375; the total number oftransactions 375 placed by each user 305; the date and/or time eachtransaction 375 was scheduled on the electronic platform 330; the totalmonetary value of each transaction 375 and/or the sale price of eachitem 310 included in each of the transaction 375; the basket size ofeach transaction 375; the rate at which each user 305 placestransactions 375; whether the user selected a pick-up and/or deliveryoption for the transaction 375; and/or a time slot that was selected forpick-up and/or delivery option.

The historical data 370 also may include, or be used to derive, othermetrics which indicate: how recently each of the items 310 werepurchased; transient item quantity selections (e.g., which may vary dueto changing family size, an occurrence of a pandemic, etc.); whetherusers 305 are loyal to particular brands; whether users 305 are willingto order alternative items 310 in the same item type category (e.g., dueto inventory shortages); whether users 305 tend to order various itemsin the same item type category (e.g., to test new items or brands); userpurchase frequency for each of the items 310 via an online or electronicchannel; user purchase frequency for each of the items 310 via offlineor brick-and-mortar channels; units of measurements for purchased items310 (e.g., indicating the sizes of items in terms of ounces, fluidounces, grams, etc.); customer's item purchase quantity patterns; iteminter-purchase intervals (IPI) (e.g., indicating the mean and/or mediantime between purchases for each of the items 310); perishability indicesassociated with purchased items 310; and/or other related factors.

As explained in further detail below, the machine learning architecture350 can extract various features from the historical data 370 togenerate feature vectors for the users 305. A plurality of featuresvectors (e.g., thousands or millions) can be utilized to train themachine learning architecture 350 to generate the basket sizepredictions 351, item quantity predictions 352, and/or other types ofpredictions. During inference, a feature vector for a user 305 can bereceived by the machine learning architecture 350 to generate thepredictions for user 305 with accuracy and in a manner that personalizesthe predicted baskets for the user 305.

The configuration of the machine learning architecture 350 can vary. Incertain embodiments, the machine learning architecture 350 can includeone or more machine learning models and/or artificial neural networksthat are configured to execute deep learning functions, artificialintelligence (AI) functions, and/or machine learning functions togenerate the predictions and perform the functions described herein.

As described in further detail below, in certain embodiments, themachine learning architecture 350 can utilize one or more random forestdecision tree models and/or one or more gradient boosted regressionmodels that are trained to learn the user transaction patterns andgenerate the basket size predictions 351, item quantity predictions 352,and/or other types of predictions. For example, in certainconfigurations, the machine learning architecture 350 may execute one ormore gradient boosted regression models to generate the basket sizepredictions 351 and one or more random forest decision tree models togenerate the item quantity predictions 352. Exemplary configurations forthe random forest decision tree models and gradient boosted regressionmodels are described in further detail below. Other types of machinelearning models also can be utilized by the machine learningarchitecture 350 to perform the functions described in this disclosure.

The machine learning architecture 350 and/or electronic platform 330 canperform the functions described herein (e.g., related to generatingpredicted baskets 365, basket size predictions 351, item quantitypredictions 352, etc.) for any of the users 305. In certain embodiments,these functions are only provided to users 305 who are active customersand/or routinely utilize the electronic platform 330 to placetransactions 375. For example, in certain embodiments, the machinelearning architecture 350 (or other component of the system 300) mayinitially analyze the historical data 370 to identify a subset of userswho routinely and/or regularly utilize the electronic platform 330,and/or who routinely and/or regularly conduct transactions 375 forparticular types of items (e.g. groceries and/or household items). Themachine learning architecture 350 can generate the predictions for thissubset of users.

The techniques described herein for generating predicted baskets 365,basket size predictions 351, and/or item quantity predictions 352 can beutilized to facilitate repurchasing of any type of item 310 (e.g., items310 relating to furniture, apparel, kitchenware, fashion, appliances,sporting goods, electronics, software, and/or other types of items 310).In certain embodiments, these techniques may be particularly useful inconnection with facilitating the repurchases of items 310 that areroutinely ordered and/or restocked by users 305, such as groceries andhousehold items (e.g., paper towels, tissues, toilet paper, toiletries,etc.).

The machine learning architecture 350 can utilize technical improvementsin machine learning technologies to optimize the predicted baskets 365,basket size predictions 351, and/or item quantity predictions 352 in amanner that increases both satisfaction of users 305 (e.g., by selectingoptimal basket sizes and quantities of items 310 to facilitate quick andeasy repurchasing of items 310) and providers of the electronic platform330 (e.g., by ensuring customer retention, preventing lost sales, andincreasing repurchase rates). For example, in certain embodiments, themachine learning architecture 350 may enhance the generation of thepredicted baskets 365, basket size predictions 351, and/or item quantitypredictions 352 by learning user transaction patterns from thehistorical data 370. The learned user transaction patterns can then beused to accurately predict the items 310 included in the predictedbaskets 365, the sizes of the predicted baskets 365, and the quantitiesof each item 310 included in the baskets.

FIG. 4 is a block diagram illustrating a detailed view of an exemplarysystem 300 in accordance with certain embodiments. The system 300includes one or more storage modules 401 that are in communication withone or more processing modules 402. The one or more storage modules 401can include: (i) non-volatile memory, such as, for example, read-onlymemory (ROM) or programmable read-only memory (PROM); and/or (ii)volatile memory, such as, for example, random access memory (RAM),dynamic RAM (DRAM), static RAM (SRAM), etc. In these or otherembodiments, storage modules 401 can comprise (i) non-transitory memoryand/or (ii) transitory memory. The one or more processing modules 402can include one or more central processing units (CPUs), graphicalprocessing units (GPUs), controllers, microprocessors, digital signalprocessors, and/or computational circuits. The one or more storagemodules 401 can store data and instructions associated with providing anelectronic platform 330, machine learning architecture 350 (andassociated sub-components), and digital shopping carts 365. The one ormore processing modules 402 can be configured to execute any and allinstructions associated with implementing the functions performed bythese components. Exemplary configurations for each of these componentsare described in further detail below.

The exemplary electronic platform 330 of system 300 includes one or moredatabases 410. The one or more databases 410 store data and informationrelated to items 310 (e.g., products and/or services) that are offeredor made available via the electronic platform 330. For example, for eachitem 310, data associated with the item 310 can include any or all ofthe following: an item name or title, an item category associated withthe item, a price, one or more customer ratings for the item, an itemdescription, images corresponding to the item, a number of total sales,and various other data associated with the item. In some embodiments,the items 310 may pertain to grocery items 411 (e.g., such as food,drinks, edible products, etc.).

The one or more databases 410 also may store the historical data 370which, as mentioned above, can include any data associated withtransactions 375 conducted by the users 305. In certain embodiments, thehistorical data 370 may store, or may be used to derive, one or morecustomer features 420, one or more item features 425, and/or one or morebasket features 430. The customer features 420 can include descriptorspertaining to the users' 305 interactivity with the electronic platform330 and/or items 310, as well as transactions patterns associated withthe users 305. The basket features 430 can include descriptorspertaining to the contents of baskets or digital shopping carts 360purchased in connection with the transactions 375 and/or items 310included in the baskets or digital shopping carts 360. The item features425 can include descriptors pertaining to the items 310.

For each user 305, exemplary customer features 420 can include any orall of the following: a total number of transactions 375 placed by theuser 305; how frequently the user 305 places transactions via theelectronic platform 330 (e.g., whether the user 305 has a highengagement level with the electronic platform 330); a typical basketsize for transactions 375 placed by the user 305 (e.g., an average ormedium basket size of the user); a minimum basket size for transactions375 placed by the user 305; a maximum basket size for transactions 375placed by the user 305; a user IPI (e.g., indicating the mean and/ormedian time between transactions 375 placed by the user); a transactionor ordering rate for the user (e.g., indicating how frequently the userplaces transactions 375 for online transactions and/or offlinetransactions); an item ordering rate (e.g., indicating how frequentlythe user 305 purchases each item 310); a time period that has lapsedsince the user placed a most recent transaction 375; and/or othermetrics associated with the user 305.

For each user 305, exemplary basket features 430 can include any or allof the following: how recently the user 305 placed a transaction 310;how recently the user 305 purchased each item 310; a total number ofitems 310 ordered on the electronic platform 330; a total number ofunique items 310 ordered on the electronic platform 330; a total numberand/or percentage of items 310 repurchased on the electronic platform330; a diversity index indicating how heterogeneous a basket is (e.g., abasket containing multiple quantities of the same/similar items has lowitem diversity); and/or other metrics related to the composition ofbaskets and/or digital shopping carts 360 or transactions 375 placed onthe electronic platform 330.

Exemplary item features 425 can include any or all of the following:units of measurement associated with items 310 (e.g., indicating sizes,weights, ounces, etc. of the items 310); organic indices associated withitem 310 (e.g., indicating whether or not the item is organic);perishability indices associated with items (e.g., indicating whether ornot items are perishable and/or the level of perishability of the items310); value pack indicators (e.g., indicating whether or not the itemcomprises a bundle of sub-items and/or how many sub-items are includedin the item); prices or purchase values for the items 310; and/or othermetrics related to the items 310.

The historical data 370 (including the customer features 420, itemfeatures 425, and basket features 430) can be derived from both onlinetransactions (e.g., transactions 375 conducted on the electronicplatform 330 and/or over a network 315) and offline transactions (e.g.,transactions conducted at physical, brick-and-mortar locations). Thisallows the machine learning architecture 350 to assess comprehensiveinformation for historical customer and purchase patterns, which canhelp to improve the accuracy of the predictions made by the machinelearning architecture 350.

The machine learning architecture 350 may generate feature vectors 440based on the historical data 370, including the customer features 420,item features 425, and/or basket features 430. Each feature vector 440may represent a one-dimensional vector and/or multi-dimensional vector.A plurality of feature vectors 440 can initially be used to train themachine learning models (e.g., one or more gradient boosted regressionmodels 455 and/or one or more random forest decision tree models 465).During inference, a feature vector 440 specific to each user can be usedto generate the basket size prediction 351 and item quantity prediction352 for the user. The feature vectors 440 used to generate the basketsize predictions 351 and item quantity predictions 352 can includedifferent features as described below.

In certain embodiments, the machine learning architecture 350 includesone or more basket composition models 450 that are configured togenerate the basket size predictions 351. The basket composition models450 can account for a multitude of factors in selecting the number ofrepurchase items 310 to be included in a predicted basket 365. Forexample, the basket size predictions 351 generated by the one or morebasket composition models 450 can be personalized for each user based onhow recently the user placed a transaction 375, the user's iteminter-purchase interval (e.g., the time between transactions conductedfor the same items 310 or category of items 310), whether the items werepreviously selected for repurchase, the price and size of the user'slast transaction 375 or basket, and/or other factors. For example, auser who recently repurchased a large quantity of items is likely torepurchase less items in the subsequent transaction. The basketcomposition models 450 can be trained to predict the optimum number ofitems 310 to include in a predicted basket 365 by accounting for theseand other factors.

In certain embodiments, the basket composition model 450 can include oneor more gradient boosted regression models 455 and/or one or more randomforest decision tree models 465 that are configured to generate thebasket size predictions 351. Other learning models also may be utilizedto generate the basket size predictions 351. In certain embodiments, thegradient boosted regression models 455 can be trained and evaluated on ahistorical data 370 for a previous time period (e.g., previous thirteenmonths of transactions) for thousands or millions of users.

Exemplary configurations for the gradient boosted regression model 455are described below. In some scenarios, the basket composition model 450may include a pair of separately trained gradient boosted regressionmodels 455. One of the gradient boosted regression models 455 can beconfigured to generate a repurchase basket size RBS value thatrepresents the basket size prediction 351 for a user 305. The repurchasebasket size RBS can be used to select the number of unique items 310included in the predicted basket 365.

The other gradient boosted regression model 455 can be configured togenerate a basket size BS value, that represents the number of uniqueitems in a basket. This includes repurchase basket size RBS, which is anumber of unique items 310 included in the predicted basket 365, plus anumber of additional unique items, newly purchased by a user 305. Basketsize BS can be used to recommend new items to a user 305 outside of thepredicted baskets 365.

The relationship between value BS and value RBS is explained in thefollowing example. For example, take the case of a user who buys thefollowing items: [chocolate_10, chocolate_10, milk_1, milk_1, eggs_1],where_num is identifier of an item type. Hypothetically, if the user hasmilk_1, eggs_1 in their purchase history, the user's repurchase basketsize RBS would equal 2 (milk_1, eggs_1), and the user's basket size BS,which is a sum of repurchase basket size RBS (2) and the new unique itemcount (chocolate_10) would be equal to 3.

Given a user's features X (e.g. the feature vector 440 extracted fromuser's historical data 370 for both online and offline transactions375), the two gradient boosted regression models 455 can be configuredto predict RBS and BS values, respectively. The gradient boostedregression models 455 can be constructed as an ensemble of K weakregressors, and can be implemented with decision trees. Prediction ofindividual tree i can be computed given input X, by making a sequence ofdecisions based on the tree structure and values in X, until a leaf nodeis reached. Each leaf node in a tree i, corresponding to X, includes themean of labels from the training samples which belong to that leaf, andthis is the value ŷ_(i). Using Equation 1 below, the gradient boostedregression models 455 can output continuous value Ŷ which is an averageover the predictions of the individual trees:

$\begin{matrix}{\hat{Y} = {\frac{\sum\limits_{i = 1}^{K}{\overset{\hat{}}{y}}_{i}}{K}.}} & (1)\end{matrix}$

Gradient boosted regression models 455 can be configured to generate 98%confidence intervals (Cis) for RBS and BS. Confidence intervals for RBScan be used to determine how conservative a value RBS from the gradientboosted regression models 455 will be applied in the selection of uniqueitems 310 to be included in the predicted basket 365. For example, broadCIs indicate lower confidence in the generated RBS value, which mightsubsequently be used to decrease the number of items included in thepredicted basket 365.

In other embodiments, one or more random forest decision tree models 465may be utilized to generate the repurchase basket size RBS and basketsize BS values. Exemplary details for random forest decision tree models465 are described in further detail below in connection with thequantity prediction models 460.

The particular feature vectors 440 utilized by the learning models ofthe basket composition model 450 for training and inference can vary. Incertain embodiments, the learning models associated with the basketcomposition model 450 can receive a feature vector 440 that comprisessome or all of the customer features and/or basket features describedherein. Other features also may be included in the feature vectors 440.

FIG. 6 is a flow diagram illustrating exemplary operations that may beexecuted by the machine learning architecture 350 to generate apredicted basket for a user according to certain embodiments.

In block 610, a user's historical purchase patterns are received by themachine learning architecture. As mentioned above, the user's historicalpurchase patterns can be extracted from the historical data (FIGS. 3-4 )and features related to the purchase patterns may be incorporated into afeature vector 440 (FIG. 4 ) for the user.

In block 620, the machine learning architecture 350 utilizes thehistorical purchase patterns and/or feature vector 440 (FIG. 4 ) togenerate a list of candidate repurchase items and likelihood scores 470(FIG. 4 ) for each of the candidate repurchase items. The likelihoodscore 470 (FIG. 4 ) for each item indicates a likelihood or probabilitythat the user will repurchase the item for the predicted basket beinggenerated. In certain embodiments, each likelihood score 470 (FIG. 4 )may represent a value between 0 and 1, where scores closer to 1 indicatea higher probability of repurchasing the item and scores closer to 0indicate a lower probability of repurchasing the item. In certainembodiments, the likelihood scores 470 (FIG. 4 ) can be generated usingthe techniques described in U.S. patent application Ser. No. 16/779,254filed on Jan. 31, 2020 (which, as mentioned above, is incorporated byreference in its entirety). After the likelihood scores 470 (FIG. 4 )are generated, the candidate repurchase items may be ordered fromhighest to lowest based on the likelihood scores 470 (FIG. 4 )associated with the items.

In block 630, the machine learning architecture 350 executes a basketcomposition model 450 (FIG. 4 ), by, among other things, utilizing thehistorical purchase patterns and/or feature vector 440 (FIG. 4 ), togenerate a basket size prediction 351 (FIGS. 3-4 ) for the predictedbasket. As mentioned above, the basket composition model 450 may includeone or more gradient boosted regression models 455 (FIG. 4 ) and/or oneor more random forest decision tree models 465 (FIG. 4 ). The basketcomposition model 450 outputs the basket size prediction RBS 351 (FIGS.3-4 ), which indicates how many unique items are to be included in thepredicted basket. In some cases, the basket composition model 450 alsomay output the basket size BS value which can be used to recommend newitems to the user.

In block 640, the items to be included in the predicted basket areselected. In certain embodiments, the basket size prediction 351 (FIGS.3-4 ) generated in block 630 indicates a number of items N, which may beRBS or RBS adjusted based on predictions of confidence intervals, to beselected, and the top N candidate repurchase items from block 620 (whichcan be ordered based on the likelihood scores 470 (FIG. 4 )) areselected for inclusion in the predicted basket.

In block 650, the predicted basket from block 640 is displayed to theuser on a GUI and includes the selected repurchase items. The number ofunique repurchase items included in the predicted basket corresponds tothe number of items indicated by the basket size prediction 351 (FIGS.3-4 ).

Returning to FIG. 4 , the machine learning architecture 350 includes oneor more quantity prediction models 460 that are configured to generatethe item quantity predictions 352. For example, for each item 310 thatis included in the predicted basket 365, the quantity prediction model460 determines or predicts the desired quantity for the item 310 and aGUI that displays the predicted basket to a user will be auto-populatedto include the predicted quantity.

Accurately predicting the purchase quantities desired by a user foritems 310 included in a predicted basket 365 can be technicallychallenging because the quantity prediction model 460 may need toaccount for various factors that can alter the desired quantity of anitem. For example, in certain embodiments, the quantity desired by theuser may vary based on factors including: 1) how recently the item waspurchased by the user (e.g., the user may wish to purchase a smallerquantity if the user recently purchased the item in a previoustransaction); 2) the unit of measurement for the item (e.g., a user maydesire a single item if the item is available in a large size or maydesire multiple items if the item is a smaller size); 3) whether theitem is an individual item or sold as a value pack that includes abundle of multiple sub-items; 4) transient quantity preferences (e.g., auser may desire a larger quantity of items in times of a pandemic,holiday trends, or increased family size); and/or 5) inventoryavailability (e.g., a user may be willing to accept alternative brandsif a preferred brand is not available, but the user may desire lowerquantities of the alterative brand). In certain embodiments, thequantity prediction model 460 can be trained to account for these andother factors that affect the quantities desired by a user in order toaccurately generate the item quantity predictions 352.

In certain embodiments, the quantity prediction model 460 can beconfigured to create a probability distribution of a user's previousitem-specific purchase quantities and utilize the purchase quantityinformation as a prior to predict the purchase quantity for the user'snext transaction. Because the predicted items and correspondingquantities are personalized, there is a high probability of the customeraccepting all the proposed items and their respective quantities in asingle click (or tap gesture). Moreover, this can serve to improvecustomer engagement and increase customer retention.

In certain embodiments, the quantity prediction model 460 can includeone or more random forest decision tree models 465 and/or one or moregradient boosted regression models 455 that are configured to generatethe item quantity predictions 352. Exemplary configurations of thesemodels are described below. Other learning models also may be utilizedto generate the item quantity predictions 352.

In certain embodiments, the quantity prediction model 460 utilizes arandom forest decision tree model 465 to generate the item quantitypredictions 352. The problem to predict the purchase quantity for eachitem 310 given the feature vector for a user can be modeled viaregression using a random forest regression technique. The random forestdecision tree model 465 can represent a machine learning model that isbuilt using an ensemble of B weak regressors. In this case, the B weakregressors can be configured to output a continuous value. Theprediction for a new sample X (e.g., a feature vector 440 containingfeatures extracted from the historical data 370 for a user) can beobtained by averaging the predictions from the B trees.

$\begin{matrix}{{y < {- {f(X)}}} = {\frac{1}{B}{\sum\limits_{b = 1}^{B}{f_{b}(X)}}}} & (2)\end{matrix}$wherein:y=prediction for new sample X;f(x)=prediction for new sample X;f_(b)(x)=prediction for new sample X from each individualtree/regressor;B=number of trees/regressors; andb=each tree/regressor from the B number of trees.

Alternatively, or additionally, the quantity prediction model 460 canutilize a gradient boosted regression model 455 to generate the itemquantity predictions 352. As explained above, the gradient boostedregression model 455 can include an ensemble of weak regressors. Duringtraining, the gradient boosted regression model 455 can receive thefeature vectors 440 and corresponding labels y, and learn a sequence ofK decision trees via a boosting method. The parameters of the gradientboosted regression model 455, which include tree depth and number K canbe declared at the beginning of the training process. During inference,prediction 9 of each individual tree i in ensemble of size K is computedgiven input vector. A leaf corresponding to the input vector includesthe mean of labels from the training samples that belong to leaf 9,which is a subset of y. Equation 1 (above) can be used to compute thesevalues and generate the item quantity prediction 352.

The particular feature vectors 440 utilized by the learning models ofthe quantity prediction model 460 for training and inference can vary.In certain embodiments, the learning models associated with the quantityprediction model 460 can receive a feature vector 440 for each user thatcomprises the customer features and/or items features described herein.Other features also may be included in the feature vectors 440 incertain embodiments.

In certain embodiments, the learning models of the quantity predictionmodel 460 can be been trained on several months of purchase historiesfrom thousands or millions of users and evaluated on the purchases ofthe same users in the following two-week period.

FIG. 7 is a flow diagram illustrating exemplary operations that may beexecuted by the machine learning architecture to generate a predictedbasket for a user according to certain embodiments.

In block 710, a user's historical purchase patterns are received by themachine learning architecture. As mentioned above, the user's historicalpurchase patterns can be extracted from the historical data 370 (FIGS.3-4 ) and features related to the purchase patterns may be incorporatedinto a feature vector 440 (FIG. 4 ) for the user.

In block 720, a basket composition model 450 (FIG. 4 ) is configuredfrom the user's historical purchase patterns of block 710 to generate abasket size prediction 351 (FIGS. 3-4 ) that determines the size and/ornumber of unique items included in the predicted basket for the user.

In block 730, a quantity prediction model 460 (FIG. 4 ) is configuredfrom the user's historical purchase patterns of block 710 to generate anitem quantity prediction 352 (FIGS. 3-4 ) for each item included in thepredicted basket. The item quantity prediction 352 determines the numberor quantity of each item to be included in the predicted basket for theuser.

In block 740, the predicted basket is generated based on the basket sizeprediction 351 (FIGS. 3-4 ) and item quantity prediction 352 (FIGS. 3-4) of blocks 720 and 730, respectively. The predicted basket can bedisplayed on a GUI to the user.

FIG. 5 is an exemplary interface 500 that may be generated and displayedin connection with providing a predicted basket to a user. The interface500 displays a listing of items 310 that have been automaticallyselected by the machine learning architecture based on the predictedpreferences of a user.

The interface includes a basket size indicator 510 specifying the sizeof the predicted basket 365 and/or the number of unique items includedin the predicted basket 365. In this example, the predicted basketincludes fifteen items (not all items are shown, but the user may scrollto view all of the other items). The basket size indicator 510 (andnumber of unique items included in the predicted basket 365) are basedon the basket size prediction 351 (FIGS. 3-4 ) generated by the machinelearning architecture (e.g., the basket composition model).

Each item 310 includes an option 520 that can be manipulated to specifywhether the user wants to include or exclude the item 310 from thepredicted basket 365. In certain embodiments, the options 520 arepre-selected by default to enable the items 310 to be quickly added to adigital shopping cart 360. A user can unselect any items he or shewishes to exclude.

Each item 310 also includes a quantity indicator 530 that indicates thepredicted quantity 535 of each item 310 included in the predicted basket365. In this example, all of the predicted quantities 535 are set toone, expect for one of the items which is set to four. The quantityindicators 530 displayed on the interface are based on the item quantitypredictions 352 (FIGS. 3-4 ) generated by the machine learningarchitecture (e.g., the quantity prediction model). If desired, a usercan select the “+” and “−” options on the quantity indicators 530 toincrease or decrease the quantities of the items 310.

After a user is satisfied with the contents of the predicted basket 365,the user can select a submission option 540 (labeled “Add all to cart”)and all of the selected items 310 will be added to the user's digitalshopping cart 360. The basket size prediction and item quantitypredictions used to generate the predicted basket 365 serve to minimizethe user interaction related to selecting and/or reordering the items310. In many cases, these predictions permit users to fill their digitalshopping carts quickly and easily with desired items using a single step(e.g., by selecting a single submission option or other option to addthe items to the digital shopping cart).

FIG. 8 illustrates a flow chart for an exemplary method 800, accordingto certain embodiments. Method 800 is merely exemplary and is notlimited to the embodiments presented herein. Method 800 can be employedin many different embodiments or examples not specifically depicted ordescribed herein. In some embodiments, the activities of method 800 canbe performed in the order presented. In other embodiments, theactivities of method 800 can be performed in any suitable order. Instill other embodiments, one or more of the activities of method 800 canbe combined or skipped. In many embodiments, system 300 (FIGS. 3-4 ),machine learning architecture 350 (FIGS. 3-4 ), electronic platform 330(FIGS. 3-4 ), and/or basket composition model 450 (FIG. 4 ) can besuitable to perform method 800 and/or one or more of the activities ofmethod 800. In these or other embodiments, one or more of the activitiesof method 800 can be implemented as one or more computer instructionsconfigured to run at one or more processing modules and configured to bestored at one or more non-transitory memory storage modules. Suchnon-transitory memory storage modules can be part of a computer systemsuch as system 300 (FIGS. 3-4 ), machine learning architecture 350(FIGS. 3-4 ), electronic platform 330 (FIGS. 3-4 ), and/or basketcomposition model 450 (FIG. 4 ). The processing module(s) can be similaror identical to the processing module(s) described above with respect tocomputer system 100 (FIG. 1 ).

In many embodiments, method 800 can comprise an activity 810 ofgenerating a feature vector for a user based, at least in part, onhistorical data pertaining to the user's previous transactions. Thefeature vector may include any features described or mentioned in thisdisclosure, including any of the customer features, item features,and/or basket features.

Method 800 can further comprise an activity 820 of generating likelihoodscores for a plurality of items. The likelihood score for each item canindicate a likelihood or probability that the user will repurchase theitem for a predicted basket being generated.

Method 800 can further comprise an activity 830 of generating a basketsize prediction for the predicted basket based, at least in part, on thefeature vector for the user. The basket size prediction can be generatedusing a basket composition model of the machine learning architecture(e.g., one or more random forest decision tree models and/or one or moregradient boosted regression models). The basket size predictionindicates or predicts a number of items to be included in the predictedbasket based on the feature vector for the user.

Method 800 can further comprise an activity 840 of populating thepredicted basket with one or more items based on the basket sizeprediction and the likelihood scores. The basket size prediction can beused to select the number of the one or more items included in thepredicted basket. The likelihood scores can be utilized to select theone or more items to be included in the predicted basket from theplurality of items. For example, given a basket size predictionindicating that L items should be included in the predicted basket, theL items having the highest likelihood scores may be selected from theplurality of items and included in the predicted basket.

FIG. 9 illustrates a flow chart for an exemplary method 900, accordingto certain embodiments. Method 900 is merely exemplary and is notlimited to the embodiments presented herein. Method 900 can be employedin many different embodiments or examples not specifically depicted ordescribed herein. In some embodiments, the activities of method 900 canbe performed in the order presented. In other embodiments, theactivities of method 900 can be performed in any suitable order. Instill other embodiments, one or more of the activities of method 900 canbe combined or skipped. In many embodiments, system 300 (FIGS. 3-4 ),machine learning architecture 350 (FIGS. 3-4 ), electronic platform 330(FIGS. 3-4 ), and/or quantity prediction model 460 (FIG. 4 ) can besuitable to perform method 900 and/or one or more of the activities ofmethod 900. In these or other embodiments, one or more of the activitiesof method 900 can be implemented as one or more computer instructionsconfigured to run at one or more processing modules and configured to bestored at one or more non-transitory memory storage modules. Suchnon-transitory memory storage modules can be part of a computer systemsuch as system 300 (FIGS. 3-4 ), machine learning architecture 350(FIGS. 3-4 ), electronic platform 330 (FIGS. 3-4 ), and/or quantityprediction model 460 (FIG. 4 ). The processing module(s) can be similaror identical to the processing module(s) described above with respect tocomputer system 100 (FIG. 1 ).

In many embodiments, method 900 can comprise an activity 910 ofgenerating a feature vector for a user based, at least in part, onhistorical data pertaining to the users' previous transactions. Thefeature vector may include any features described or mentioned in thisdisclosure, including any of the customer features, item features,and/or basket features.

Method 900 can further comprise an activity 920 of selecting one or moreitems to be included in a predicted basket for the user.

Method 900 can further comprise an activity 930 of generating an itemquantity prediction for each of the one or more items included in thepredicted basket based, at least in part, on the feature vector for theuser. In certain embodiments, the item quantity predictions may begenerated using a quantity prediction model of the machine learningarchitecture (e.g., a random forest decision tree model and/or agradient boosted regression model described above). For each itemincluded in the predicted basket, the item quantity prediction predictspurchase quantity desired by the user.

Method 900 can further comprise an activity 940 of populating a quantityselection option for each of the one or more items included in thepredicted basket based on the item quantity prediction generated foreach of the one or more items.

As evidenced by the disclosure herein, the techniques set forth in thisdisclosure are rooted in computer technologies that overcome existingproblems in known prediction systems, specifically problems dealing withproviding accurate predictions in connection with generating predictedbaskets. The techniques described in this disclosure provide a technicalsolution (e.g., one that utilizes various machine learning techniques)for overcoming the limitations associated with known techniques. Forexample, the prediction techniques described herein take advantage ofnovel machine learning techniques to learn functions for selecting itemsfor predicted baskets, and predicting optimal sizes and item quantitiesfor predicted baskets. This technology-based solution marks animprovement over existing capabilities and functionalities related tocomputer systems by improving the accuracy and quality of thepredictions, and personalizing the predictions for each of the users.

In certain embodiments, the techniques described herein canadvantageously improve user experiences with electronic platforms byquickly generating predictions with high accuracy that enable users toeasily finalize digital shopping carts in connection with repurchasingor restocking items. In various embodiments, the techniques describedherein can be executed dynamically in real time by an electronicplatform. In many embodiments, the techniques described herein can beused continuously at a scale that cannot be reasonably performed usingmanual techniques or the human mind (e.g., due to the large numbers ofcustomers, large quantity of features that are extracted from historicaldata for each of the customer, and complex operations executed by themachine learning architecture). The data analyzed by the machinelearning techniques described herein can be too large to be analyzedusing manual techniques.

Furthermore, in a number of embodiments, the techniques described hereincan solve a technical problem that arises only within the realm ofcomputer networks, because machine learning does not exist outside therealm of computer networks.

Although systems and methods have been described with reference tospecific embodiments, it will be understood by those skilled in the artthat various changes may be made without departing from the spirit orscope of the disclosure. Accordingly, the disclosure of embodiments isintended to be illustrative of the scope of the disclosure and is notintended to be limiting. It is intended that the scope of the disclosureshall be limited only to the extent required by the appended claims. Forexample, to one of ordinary skill in the art, it will be readilyapparent that any element of FIGS. 1-9 may be modified, and that theforegoing discussion of certain of these embodiments does notnecessarily represent a complete description of all possibleembodiments. For example, one or more of the procedures, processes, oractivities of FIGS. 8-9 may include different procedures, processes,and/or activities and be performed by many different modules, in manydifferent orders.

All elements claimed in any particular claim are essential to theembodiment claimed in that particular claim. Consequently, replacementof one or more claimed elements constitutes reconstruction and notrepair. Additionally, benefits, other advantages, and solutions toproblems have been described with regard to specific embodiments. Thebenefits, advantages, solutions to problems, and any element or elementsthat may cause any benefit, advantage, or solution to occur or becomemore pronounced, however, are not to be construed as critical, required,or essential features or elements of any or all of the claims, unlesssuch benefits, advantages, solutions, or elements are stated in suchclaim.

Moreover, embodiments and limitations disclosed herein are not dedicatedto the public under the doctrine of dedication if the embodiments and/orlimitations: (1) are not expressly claimed in the claims; and (2) are orare potentially equivalents of express elements and/or limitations inthe claims under the doctrine of equivalents.

What is claimed is:
 1. A system comprising: one or more processors; andone or more non-transitory computer-readable media storing computinginstructions that, when executed on the one or more processors, causethe one or more processors to perform functions comprising: generating afeature vector for a user based, at least in part, on historical datapertaining to previous transactions of the user, wherein the featurevector comprises basket features; generating, using a quantityprediction model of a machine learning architecture, a respective itemquantity prediction for each of one or more items included in apredicted basket based, at least in part, on the feature vector for theuser by: creating a probability distribution of previous item-specificpurchase quantities of the user; and utilizing previous quantity data asa prior probability to predict a purchase quantity for each of the oneor more items for a future transaction of the user; and populating arespective quantity selection option for each of the one or more itemsincluded in the predicted basket based on the respective item quantityprediction generated for each of the one or more items.
 2. The system ofclaim 1, wherein: the respective item quantity prediction for each ofthe one or more items indicates a respective purchase quantity for eachof the one or more items to be included in the predicted basket; and thepredicted basket is automatically populated with the one or more itemsand the respective purchase quantity associated with each of the one ormore items.
 3. The system of claim 1, wherein the quantity predictionmodel comprises a random forest decision tree model that is configuredto generate the respective item quantity prediction for each of the oneor more items, and the random forest decision tree model is trainedusing historical data for a plurality of users.
 4. The system of claim1, wherein the quantity prediction model comprises a gradient boostingregression model that is configured to generate the respective itemquantity prediction for each of the one or more items, and the gradientboosting regression model is trained using historical data for aplurality of users.
 5. The system of claim 1, wherein the feature vectorat least includes: customer features associated with the previoustransactions of the user involving the one or more items; and itemfeatures describing the one or more items; and wherein the basketfeatures comprise: descriptors associated with contents of baskets ordigital shopping carts, wherein the descriptors comprise: a percentageof items repurchased on an electronic platform; a diversity indexshowing whether the contents of the baskets or the digital shoppingcarts comprise a heterogeneous basket; or a metric associated with acomposition of the baskets.
 6. The system of claim 1, wherein thepredicted basket and the respective quantity selection option for eachof the one or more items is displayed on a graphical user interface of auser device operated by the user.
 7. The system of claim 6, where in thegraphical user interface includes a submission option that enables theone or more items included in the predicted basket to be added to adigital shopping cart.
 8. The system of claim 6, where in the graphicaluser interface includes one or more options for manipulating contents ofthe predicted basket including: options for changing purchase quantitiesof the one or more items; and options for removing the one or more itemsfrom the predicted basket.
 9. The system of claim 1, wherein: themachine learning architecture is integrated into an electronic commerceplatform that is accessible over a computer network; the one or moreitems correspond to one or more grocery items; and the predicted basketfacilitates repurchasing of the one or more grocery items.
 10. Thesystem of claim 1, wherein: the machine learning architecture furthercomprises a basket composition model; the basket composition model isconfigured to generate a basket size prediction for the predictedbasket; and the basket size prediction is used to determine a number ofunique items that are included in the predicted basket.
 11. A methodimplemented via execution of computing instructions configured to run atone or more processors and configured to be stored at non-transitorycomputer-readable media, the method comprising: generating a featurevector for a user based, at least in part, on historical data pertainingto previous transactions of the user, wherein the feature vectorcomprises basket features; generating, using a quantity prediction modelof a machine learning architecture, a respective item quantityprediction for each the one or more items included in a predicted basketbased, at least in part, on the feature vector for the user by: creatinga probability distribution of previous item-specific purchase quantitiesof the user; and utilizing previous quantity data as a prior probabilityto predict a purchase quantity for each of the one or more items for afuture transaction of the user; and populating a respective quantityselection option for each of the one or more items included in thepredicted basket based on the respective item quantity predictiongenerated for each of the one or more items.
 12. The method of claim 11,wherein: the respective item quantity prediction for each of the one ormore items indicates a respective purchase quantity for each of the oneor more items to be included in the predicted basket; and the predictedbasket is automatically populated with the one or more items and therespective purchase quantity associated with each of the one or moreitems.
 13. The method of claim 11, wherein the quantity prediction modelcomprises a random forest decision tree model that is configured togenerate the respective item quantity prediction for each of the one ormore items, and the random forest decision tree model is trained usinghistorical data for a plurality of users.
 14. The method of claim 11,wherein the quantity prediction model comprises a gradient boostingregression model that is configured to generate the respective itemquantity prediction for each of the one or more items, and the gradientboosting regression model is trained using historical data for aplurality of users.
 15. The method of claim 11, wherein the featurevector at least includes: customer features associated with the previoustransactions of the user involving the one or more items; and itemfeatures describing the one or more items; and wherein the basketfeatures comprise: descriptors associated with contents of baskets ordigital shopping carts, wherein the descriptors comprise: a percentageof items repurchased on an electronic platform; a diversity indexshowing whether the contents of the baskets or the digital shoppingcarts comprise a heterogeneous basket; or a metric associated with acomposition of the baskets.
 16. The method of claim 11, wherein thepredicted basket and the respective quantity selection option for eachof the one or more items is displayed on a graphical user interface of auser device operated by the user.
 17. The method of claim 16, where inthe graphical user interface includes a submission option that enablesthe one or more items included in the predicted basket to be added to adigital shopping cart.
 18. The method of claim 16, where in thegraphical user interface includes one or more options for manipulatingcontents of the predicted basket including: options for changingpurchase quantities of the one or more items; and options for removingthe one or more items from the predicted basket.
 19. The method of claim11, wherein: the machine learning architecture is integrated into anelectronic commerce platform that is accessible over a computer network;the one or more items correspond to one or more grocery items; and thepredicted basket facilitates repurchasing of the one or more groceryitems.
 20. The method of claim 11, wherein: the machine learningarchitecture further comprises a basket composition model; the basketcomposition model is configured to generate a basket size prediction forthe predicted basket; and the basket size prediction is used todetermine a number of unique items that are included in the predictedbasket.